The heat dissipated by today's computing equipment is reaching levels that make it very challenging to cool these systems in densely packed data centers or telecommunications rooms. In data centers, the computing equipment, such as a multitude of computer servers, are commonly placed in a series of racks arranged in a series of aisles in the data center. Typically, a data center has a cooling system that, e.g., by way of one or more air conditioning units (ACUs), introduces cooled air to the racks, for example, through a sub-floor plenum and associated perforated tiles in the floor above the sub-floor plenum. Without a proper layout in the data center, costly inefficiencies in the cooling system inevitably occur, and can potentially result in ineffective cooling of the equipment.
Air flow distributions within a data center have a major impact on the thermal environment of the equipment within the data center. Computational fluid dynamics (CFD) calculations have been used to solve the Navier Stokes (NS) Equations and the modeling results of the NS-CFD have been employed to thermally manage data centers. There can be, however, several potential problems associated with NS-CFD modeling of a data center. First, while NS-CFD modeling has been successfully deployed for the design of very well-defined structures, such as air plane wings, the application of NS-CFD modeling to data centers can be somewhat problematic because input data needed for NS-CFD modeling is often not available and/or is inaccurate. Namely, every data center is different and a current inventory list is often not available for each data center (further, heterogeneous technology may be used within a given data center, e.g., computer equipment from different vendors and/or of different vintages), available data (e.g., name-plate power and flow data) generally does not reflect actual usage, air flow is very difficult and time-consuming to accurately measure and characterize (and often does not capture room effects such as drafts). In all, it could easily take one person at least one week to survey a 5,000 square foot data center, which is an overly time-consuming process.
Second, a data center NS-CFD model can be difficult to generate and typically requires a detailed survey of the data center, which is a time consuming and costly process (as described above). However, even if a complicated NS-CFD model has been built, there is very little confidence that it actually gives dependable insights, because, as described above, the input data for the NS-CFD model is often not available and/or does not accurately represent the data center.
Third, the calculations involved are time-consuming (slow) and besides many assumptions, which are intrinsically built into these NS-CFD models (i.e., with CFD and other related models such as the k-epsilon turbulence model, uniform air flow rate through server rack and uniform volumetric heat generation inside of the server rack are assumed) such models cannot readily include spatial and temporal variability in the workload, as well as other unknowns, because the pure computation time of these models is quite significant.
Fourth, existing data center NS-CFD models cannot easily be used to optimize data center layout. Namely, there is no systematic strategy for changing inputs to the model based on measurements. Rather, data center optimization is done today rather unscientifically “by hand” playing a few what-ifs, e.g., where an engineer looks at results and uses his/her intuition to adjust the model.
Therefore, data center modeling techniques are needed that provide improved accuracy and efficiency over conventional processes so as to permit optimization of data center layout.